Biology subjects

Dudley, A. M.

Publications and source records attributed to Dudley, A. M..

7 recordsLinked to original sources

bioRxiv2025

Pervasive context-dependent effects in the genetic architecture of complex and quantitative traits revealed by a powerful multiparent mapping population in yeast

The genetic dissection of complex traits remains a major challenge in basic and biomedical research, but is essential for understanding the molecular pathways that shape phenotypic variation and for developing predictive models of trait and disease susceptibility. Here, we leverage a novel multiparent mapping population of budding yeast, CYClones, comprising 9,344 haploid strains derived from eight genetically diverse founders, to identify quantitative trait loci (QTL) and systematically investigate the genetic architecture of growth rates across ten environmental conditions. In total, we identified 349 QTL (ranging from 19 to 49 QTL per growth condition) that explained between 61% and 100% of narrow sense heritability across traits. The high power and resolution of CYClones revealed that growth traits exhibited distinct, condition-specific genetic architectures with extensive allelic heterogeneity, where a QTL was the result of multiple tightly linked causal variants. We also observed pleiotropy among QTL with complex, trait-dependent allele effects that are also consistent with allelic heterogeneity. Genetic complexity varied widely, with some traits showing nearly Mendelian architectures, while others were highly polygenic. Introgressed loci played a prominent role in the landscape of growth rate QTL, including one that is localized to a 2.4 kb interval in the PCA1 cadmium transporter and explains 72% of variation in cadmium resistance and another large-effect QTL that alters growth on galactose via complex non-additive interactions with the transcriptional regulator GAL3. In both cadmium and galactose conditions, we show that allelic variation at a small number of loci stratifies the population into regulatory or physiological subgroups, each with distinct genetic architectures, a phenomenon we term allele-dependent stratification. Collectively, our results provide novel insights into the genetics of growth rates in budding yeast, the architectural features of genetic complexity, and demonstrate that CYClones is a powerful platform for revealing the molecular basis of complex trait variation.

genetics↗

bioRxiv2025

CYClones: A highly powered, fully genotyped, 8-parent yeast mapping population

The budding yeast Saccharomyces cerevisiae is a remarkably adaptable organism that thrives in diverse environments. Global sequencing of natural isolates has revealed extensive genetic diversity within the species. Here, we describe the construction and characterization of CYClones (Collaborative Yeast Cross clones), a library of 11,392 segregants generated from a multiparent funnel cross of eight genetically diverse parental strains. To enable the genetic dissection of complex traits, we imputed whole-genome sequences for all segregants and show that CYClones captures a substantial fraction of the global genetic diversity of S. cerevisiae. Haplotype representation is well maintained, with each parental haplotype present at >5% frequency across >95% of the genome. Simulations demonstrate that CYClones has [≥]95% power to detect variants with heritability as low as 0.36%, with mapping resolution often finer than the length of a single gene. In summary, CYClones is a powerful community resource for dissecting the genetic architecture of complex and quantitative traits, uncovering context-dependent mutational effects, and identifying causal variants underlying phenotypic diversity.

genetics↗

bioRxiv2025

Functional Profiling of 2,193 ASS1 Missense Variants: Insights into Variant Pathogenicity and Epistatic Interactions in Citrullinemia Type I

Sequence variants in the urea cycle gene argininosuccinate synthase (ASS1) cause Citrullinemia type 1 (CTLN1), a rare autosomal recessive disease. Mechanistically, reduction in argininosuccinate synthetase (ASS) enzyme activity impairs the urea cycle, leading to an accumulation of citrulline and neurotoxic ammonia. Disease severity varies according to the degree of enzyme impairment, ranging from severe neonatal forms (classic citrullinemia) to milder, late-onset forms that may manifest in childhood or adulthood. We established a high-throughput yeast functional assay of human ASS and individually measured the impact of 2193 amino acid substitutions, representing 90% of all single nucleotide variant (SNV)-accessible substitutions. When benchmarked against existing clinical variant annotation, our assay distinguishes known benign variants from strong loss of function pathogenic variants, enabling identification of a functional score threshold below which variants show clinically relevant impairment of ASS activity. Using the ACMG OddsPath framework, our assay meets PS3_supporting criteria for pathogenicity classification and achieves full PS3-level strength when variants observed as homozygotes in other primates are used as benign proxies for calibration. These results provide direct functional evidence to inform reclassification of ASS1 missense variants, supporting their clinical interpretation and diagnostic utility. Mapping functional scores onto the protein structure, we confirmed that residues involved in catalysis are highly sensitive to substitution. In addition, we identified residues from adjacent subunits of the ASS homotetramer that form compound active sites. Assaying these positions revealed a capacity for intragenic complementation consistent with a variant sequestration model: a form of positive epistasis in which deleterious variants from different subunits are sequestered into only a subset of active sites, restoring function in the remaining variant-free sites. The discovery of intragenic complementation in ASS reveals a novel mode of functional interaction with clinical implications for interpreting variant combinations in heterozygous individuals.

genetics↗

bioRxiv2024

Spatiotemporal Patterns of Gene Expression During Development of a Complex Colony Morphology

Clonal communities of single celled organisms, such as bacterial or fungal colonies and biofilms, are spatially structured, with subdomains of cells experiencing differing environmental conditions. In the development of such communities, cell specialization is not only important to respond and adapt to the local environment but has the potential to increase the fitness of the clonal community through division of labor. Here, we examine colony development in a yeast strain (F13) that produces colonies with a highly structured "ruffled" phenotype in the colony periphery and an unstructured "smooth" phenotype in the colony center. We demonstrate that in the F13 genetic background deletions of transcription factors can either increase (dig1{Delta}, sfl1{Delta}) or decrease (tec1{Delta}) the degree of colony structure. To investigate the development of colony structure, we carried out gene expression analysis on F13 and the three deletion strains using RNA-seq. Samples were taken early in colony growth (day2), which precedes ruffled phenotype development in F13, and from the peripheral and central regions of colonies later in development (day5), at which time these regions are structured and unstructured (respectively) in F13. We identify genes responding additively and non-additively to the genotype and spatiotemporal factors and cluster these genes into a number of different expression patterns. We identify clusters whose expression correlates closely with the degree of colony structure in each sample and include genes with known roles in the development of colony structure. Individual deletion of 26 genes sampled from different clusters identified 5 with strong effects on colony morphology (BUD8, CIS3, FLO11, MSB2 and SFG1), all of which eliminated or greatly reduced the structure of the F13 outer region.

genetics↗

bioRxiv2022

The functional impact of 1,570 SNP-accessible missense variants in human OTC

Deleterious mutations in the X-linked gene encoding ornithine transcarbamylase (OTC) cause the most common urea cycle disorder, OTC deficiency. This rare, but highly actionable disease can present with severe neonatal onset in males or with later onset in either sex. Neonatal onset patients appear normal at birth but rapidly develop hyperammonemia, which can progress to cerebral edema, coma and death, outcomes ameliorated by rapid diagnosis and treatment. Existing biochemical assays have limitations, including the sensitivity of the citrulline assays used in newborn screening panels. With prior knowledge of variant pathogenicity, DNA sequence-based diagnostics would provide an alternative screening method. Here, we develop a high throughput functional assay for human OTC and measure the impact of 1,570 variants, 84% of all SNP-accessible missense mutations. Our assay scores agree well with existing clinical significance calls, distinguishing known benign from pathogenic variants and variants with neonatal onset from late-onset disease presentation. Further, use of an intronless expression construct allows us to measure the impact of amino acid changes at splice sites independent of their effect on splicing, thereby separating the contribution of splicing and protein coding changes to aid the analysis of molecular mechanisms underlying pathogenicity. Finally, we assess the utility of our functional data on OTC variant curation by using the current ACMG/AMP guidelines to reclassify variants. Inclusion of our data as PS3/BS3 substantially improves variant interpretation. Thus, our dataset is of high clinical utility and illustrates the power of functional assays to inform interpretation of existing and novel genetic variation.

genetics↗

bioRxiv2019

Computational Inference Software for Tetrad Assembly from Randomly Arrayed Yeast Colonies

Here, we describe an information-theory-based method and associated software for computationally identifying sister spores derived from the same meiotic tetrad. The method exploits specific DNA sequence features of tetrads that result from meiotic centromere and allele segregation patterns. Because the method uses only the genomic sequence, it alleviates the need for tetrad-specific barcodes or other genetic modifications to the strains. Using this method, strains derived from randomly arrayed spores can be efficiently grouped back into tetrads.

bioinformatics↗

bioRxiv2018

Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

MotivationMulticellular entities, such as mammalian tissues or microbial biofilms, typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding on how cell-cell and metabolic coupling produce functionally optimized structures is still limited.\n\nResultsHere, we present a data-driven spatial framework to computationally investigate the development of one multicellular structure, yeast colonies. Using experimental growth data from homogeneous liquid media conditions, we develop and parameterize a dynamic cell state and growth model. We then use the resulting model in a coarse-grained spatial model, which we calibrate using experimental time-course data of colony growth. Throughout the model development process, we use state-of-the-art statistical techniques to handle the uncertainty of model structure and parameterization. Further, we validate the model predictions against independent experimental data and illustrate how metabolic coupling plays a central role in colony formation.\n\nAvailabilityExperimental data and a computational implementation to reproduce the results are available at http://research.cs.aalto.fi/csb/software/multiscale/code.zip.\n\nContactjukka.intosalmi@aalto.fi, alexander.skupin@uni.lu

bioinformatics↗